AI applied to evaluate waterflood response, gas behind pipe, and imbibition stimulation treatments

During the last 10 years, the application of neural networks to solve oilfield problems has advanced. More recently, fuzzy logic proved beneficial when selecting variables to serve as neural network inputs. This paper presents a background to establish that neural networks are more than a “black box” and summarizes three applications of artificial intelligence technology to predict the secondary-to-primary ratio of a waterflood candidate using public domain information, the potential gas-producing rate of a behind pipe interval given only gamma ray and density logs, and the performance of single-well chemical imbibition treatments. cial intelligence used in this manner is essentially a statistical analysis, and as such the size of the dataset and the extent of the domain are important. Ignoring these important factors could result in overtraining of the neural network. Special care should be taken to avoid the pitfalls associated with overtraining. ural network architectures were designed using a trial-and-error technique. Initially, a constructive design was employed by adding complexity to the architecture in terms of increasing the number of input variables as well as the number of hidden layers and nodes. A technique based on conventional statistical parameters was developed to numerically describe the patterns observed in log crossplots. These numerical descriptions were then prioritized and used as neural network inputs to be correlated with known production response.